• 한국콘텐츠학회논문지
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• 제12권10호
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• pp.11-21
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• 2012

하이브리드 네트워크 인프라는 Internet2, SURFNet 등의 선도 연구망 커뮤니티에게 가장 우선적인 기술로 대두되고 있다. 그러나, 첨단(high-end) 응용의 종단 간 협업 연구를 위하여 필수적인 하이브리드 연구망 간의 인터도메인 협업 인프라는 실질적인 아키텍쳐의 설계와 구현에 있어서 아직도 많은 연구를 필요로 하고 있다. 따라서 본 논문에서는 하이브리드 연구망 기반의 분산 가상형 네트워크 운영과 리소스 정보 관리를 위한 프레임워크를 제안하고, 이를 기반으로 코어 시스템을 구현하였다. 제안된 프레임워크는 멀티도메인 하이브리드 연구망 운영과 관리를 위하여 분산형 아키텍쳐로 설계되었다. 분산 가상형 네트워크 운영 프레임워크는 네트워크 도메인 내에서 자치성과 독립적인 제어를 유지하면서 인터도메인 네트워크 간의 협업을 가능케 함으로써, 연구자 및 실험자가 스스로 생성한 가상 네트워크를 운영 관리 할 수 있는 환경을 제공할 수 있다. 본 논문에서는 제안된 프레임워크를 위한 세부적인 구조와 기술을 다루며, 이러한 환경이 어떻게 고성능 첨단(high-end) 응용을 위하여 활용될 수 있는지에 대하여 고찰한다.

• IEIE Transactions on Smart Processing and Computing
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• 제3권5호
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• pp.325-330
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• 2014

The era of convergence is being applied to all areas of Information and Communication Technology (ICT). The convergence of broadcasting service and communication service almost occurs on smart devices including smartphone. The smart hybrid Digital Multimedia Broadcasting (DMB) is a typical example of the convergence of broadcasting and wireless communication service. The hybrid mobile broadcasting service can support seamless video, 3D, high quality, and additional data services based on network connection between the broadcasting and wireless network. The gateway and terminal (including apps on the smartphone) take the role of the main components on the hybrid service. This paper presents the service concept, main components structure, the implementation of gateway and terminals, and field test to the urban areas for the mobile hybrid system.

• International Journal of Computer Science & Network Security
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• 제22권6호
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• pp.364-373
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• 2022

Image morphing methods make seamless transition changes in the image and mask the meaningful information attached to it. This can be detected by traditional machine learning algorithms and new emerging deep learning algorithms. In this research work, scope of different Hybrid learning approaches having combination of Deep learning and Machine learning are being analyzed with the public dataset CASIA V1.0, CASIA V2.0 and DVMM to find the most efficient algorithm. The simulated results with CNN (Convolution Neural Network), Hybrid approach of CNN along with SVM (Support Vector Machine) and Hybrid approach of CNN along with Random Forest algorithm produced 96.92 %, 95.98 and 99.18 % accuracy respectively with the CASIA V2.0 dataset having 9555 images. The accuracy pattern of applied algorithms changes with CASIA V1.0 data and DVMM data having 1721 and 1845 set of images presenting minimal accuracy with Hybrid approach of CNN and Random Forest algorithm. It is confirmed that the choice of best algorithm to find image forgery depends on input data type. This paper presents the combination of best suited algorithm to detect image morphing with different input datasets.

• 한국경영과학회:학술대회논문집
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• 한국경영과학회 1996년도 추계학술대회발표논문집; 고려대학교, 서울; 26 Oct. 1996
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• pp.203-206
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• 1996

Prediction of firm bankruptcy have been extensively studied in accounting, as all stockholders in a firm have a vested interest in monitoring its financial performance. The objective of this paper is to develop the hybrid models for bankruptcy prediction. The proposed hybrid models are two phase. Phase one are (a) DA-assisted neural network, (b) Logit-assisted neural network, and (c) Genetic-assisted neural network. And, phase two are (a) DA-assisted Case based reasoning, and (b) Genetic-assisted Case based reasoning. In the variables selection, We are focusing on three alternative methods - linear discriminant analysis, logit analysis and genetic algorithms - that can be used empirically select predictors for hybrid model in bankruptcy prediction. Empirical results using Korean medium-sized firms data show that hybrid models are very promising neural network models and case based reasoning for bankruptcy prediction in terms of predictive accuracy and adaptability.

• Journal of Electrical Engineering and Technology
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• 제8권6호
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• pp.1626-1636
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• 2013

Nowadays, a rapid development in wind power technologies is occurring compared with other renewable energies. This advance in technology has facilitated a new generation of wind turbines with larger capacity and higher efficiency. As the height of the turbines and the distance between turbines increases, the monitoring and control of this new generation wind turbines presents new challenges. This paper presents the architectural design, simulation, and evaluation of hybrid communication networks for a large-scale wind turbine (WT). The communication network of WT is designed based on logical node (LN) concepts of the IEC 61400-25 standard. The proposed hybrid network architectures are modeled and evaluated by OPNET. We also investigate network performance using three different technologies: Ethernet-based, WiFi-based, and ZigBee-based. Our network model is validated by analyzing the simulation results. This work contributes to the design of a reliable communication network for monitoring and controlling a wind power farms (WPF).

• Advances in Computational Design
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• 제8권1호
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• pp.37-59
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• 2023

Squat reinforced concrete (SRC) shear walls are a critical part of the structure for both office/residential buildings and nuclear structures due to their significant role in withstanding seismic loads. Despite this, empirical formulae in current design standards and published studies demonstrate a considerable disparity in predicting SRC wall shear strength. The goal of this research is to develop and evaluate hybrid and ensemble artificial neural network (ANN) models. State-of-the-art population-based algorithms are used in this research for hybrid intelligence algorithms. Six models are developed, including Honey Badger Algorithm (HBA) with ANN (HBA-ANN), Hunger Games Search with ANN (HGS-ANN), fitness-distance balance coyote optimization algorithm (FDB-COA) with ANN (FDB-COA-ANN), Averaging Ensemble (AE) neural network, Snapshot Ensemble (SE) neural network, and Stacked Generalization (SG) ensemble neural network. A total of 434 test results of SRC walls is utilized to train and assess the models. The results reveal that the SG model not only minimizes prediction variance but also produces predictions (with R²= 0.99) that are superior to other models.

• ETRI Journal
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• 제34권6호
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• pp.838-846
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• 2012

This paper proposes a cost-effective hybrid-type power budget extender (PBEx) that can provide a high power budget of over 45 dB in an asymmetric 10-Gb/s Ethernet passive optical network (10/1G-EPON). The hybrid-type 10/1G-EPON PBEx comprises a central office terminal (COT) and remote terminal (RT) module supporting four channels and uses a coarse wavelength division multiplexing (CWDM) technology between the COT and RT for a reduction of fiber cost and efficient access network design. The proposed 10/1G-EPON PBEx can provide over a 40-km reach and 128-way split per CWDM wavelength with no modification of a legacy 10/1G-EPON system and can satisfy the error-free service in $10^{10}$ packet transmission.

• 한국항해항만학회:학술대회논문집
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• 한국항해항만학회 2006년도 International Symposium on GPS/GNSS Vol.2
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• pp.101-106
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• 2006

The geoidal undulations are needed for determining the orthometric heights from the Global Positioning System GPS-derived ellipsoidal heights. There are several methods for geoidal undulation determination. The paper presents a method employing a simple architecture Two Layer Multiquadric-Biharmonic Artificial Neural Network (TLMB-ANN) to approximate an area of 4200 square kilometres quasigeoid surface with GPS-levelling data. Hardy’s Multiquadric-Biharmonic functions is used as the hidden layer neurons’ activation function and Levenberg-Marquardt algorithm is used to train the artificial neural network. In numerical examples five surfaces were compared: the gravimetric geometry hybrid quasigeoid, Support Vector Machine (SVM) model, Hybrid Fuzzy Neural Network (HFNN) model, Traditional Three Layer Artificial Neural Network (ANN) with tanh activation function and TLMB-ANN surface approximation. The effectiveness of TLMB-ANN surface approximation depends on the number of control points. If the number of well-distributed control points is sufficiently large, the results are similar with those obtained by gravity and geometry hybrid method. Importantly, TLMB-ANN surface approximation model possesses good extrapolation performance with high precision.

• 한국산업정보학회논문지
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• 제22권1호
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• pp.1-9
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• 2017

무선 네트워크 기술과 무선 전송 기술의 발전으로 인해 무선 전달망(Wireless Transit Network) 구성 기술로서 무선 메시 네트워크(WMN)가 관심을 받고 있다. 오랜 기간 다양한 영역에서 WMN에 대한 연구가 진행되었지만 아직 해결되지 않은 문제들이 많이 남아있다. 그 중 하나가 무선 링크로 구성된 다중 홉 네트워크에서 최적 경로를 찾는 라우팅 문제이다. WMN의 3가지 구성유형 중 하나인 하이브리드 WMN에서 최적 경로 선택을 위해서는 우수한 성능의 메트릭 연구와 함께 효과적으로 인프라스트럭처 메시를 전달망으로 사용하기 위한 경로 검색 프로토콜 연구가 필요하다. 따라서 본 논문에서는 하이브리드 WMN을 위한 듀얼모드-AODV(Ad hoc On-demand Distance Vector)를 제안한다. 본 논문에서 제안된 듀얼모드-AODV를 하이브리드 WMN에 적용하는 경우 AODV에 비하여 경로 검색 지연시간이 52% 감소하는 것을 시뮬레이션을 통해 확인하였다.

• 방송공학회논문지
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• 제19권5호
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• pp.687-698
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• 2014

ATSC는 방송망 기반의 서비스호환 3DTV 방송서비스 표준완료 이후 최근 이종망 환경에서 하이브리드 3DTV 방송서비스에 대한 표준화를 진행 중에 있다. 본 논문에서는 기존의 디지털방송 화질열화 없이 Full HD 3D 화질을 보장하기 위한 방송망 및 IP망 연동형 3D 비디오 방송방식을 제안한다. 특히, 본 논문에서는 ISO/IEC 23009-1 DASH를 활용한 3D 부가영상 전송, 이종망 환경 하에서 안정적인 3D 비디오 동기화 및 하이브리드 3DTV 수신기 개발을 위한 시스템 타겟 디코더 모델을 기술한다. 실험결과, 제안된 기술은 하이브리드 3DTV 방송 표준화에 직접적으로 적용될 수 있으며 안정적인 하이브리드 3DTV 인코더 및 수신기 개발을 위한 참조 모델로 활용될 수 있음을 확인하였다.